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    Öğe
    Friend Recommendation Decision Systems via Multiple Social Network Alignment
    (Institute of Electrical and Electronics Engineers Inc., 2020) Mungen, Ahmet Anil; Bulut, Betul; Kaya, Mehmet
    Today, almost all internet users have more than one social network account on different social networks for interaction with friends and other users. Gathering data from various networks to combined into a single node can be used for increasing the success rate of recommendation systems. In this study, data related to thousands of users in nine different social networks are used for successful recommendations to the users. The anchor method is used for topological alignment, and the relationship between nodes is taken into account for calculation. Also, the node similarity method is used to increase the success rate. In this method, the number of successful node matching is increased thanks to the feature selection criteria. An original node alignment and node similarity methods are proposed in the study. Because of combine both node alignment and node similarity method, the proposed method is very successful for the friend recommendation. © 2020 IEEE.
  • [ X ]
    Öğe
    Polyp Segmentation in Colonoscopy Images using U-Net and Cyclic Learning Rate
    (Ieee, 2022) Bulut, Betul; Butun, Ertan; Kaya, Mehmet
    Colonoscopy is an important procedure in the diagnosis of colorectal cancer. The use of computer aided systems has become important to support clinicians performing colonoscopy and to prevent polyps from escaping the clinician's attention. Image segmentation studies using deep learning achieves successful results and can play a crucial role on diagnosis procedure of colorectal cancer. We trained Unet architecture for polyp segmentation and determined the learning rate, one of the most important training parameters, using Cyclic Learning Rate policy. The results show that the success rate is increased in the segmentation task performed Unet with Cyclic Learning Rate policy. In this study, we have contributed to more accurate detection of polyp diagnosis, which can be a precursor to cancer, by using the UNET architecture with an effective learning rate strategy.